Suppose you detect heteroskedasicity and /or auto correlated errors in your regression. What is the difference between (I) calculating robust errors versus (ii) conducting a weighted least squares or feasible generalized least squares analysis. What are the pros and cons of each approach? Give an example of when each might be preferred over the other.